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Understanding the local structure and thermophysical behavior of Mg-La liquid alloys via machine learning potential

international Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 439-449页

作者： Jia Zhao Taixi Feng Guimin Lu National Engineering Research center for Integrated Utilization of Salt Lake Resource East China University of Science and TechnologyShanghai 200237China Joint international Laboratory for Potassium and Lithium Strategic Resources East China University of Science and TechnologyShanghai 200237China

The local structure and thermophysical behavior of Mg-La liquid alloys were in-depth understood using deep potential molecular dynamic(DPMD) simulation driven via machine learning to promote the development of Mg-La alloys. The robustness of the trained deep potential(DP) model was thoroughly evaluated through several aspects, including root-mean-square errors(RMSEs), energy and force data, and structural information comparison results;the results indicate the carefully trained DP model is reliable. The component and temperature dependence of the local structure in the Mg-La liquid alloy was analyzed. The effect of Mg content in the system on the first coordination shell of the atomic pairs is the same as that of temperature. The pre-peak demonstrated in the structure factor indicates the presence of a medium-range ordered structure in the Mg-La liquid alloy, which is particularly pronounced in the 80at% Mg system and disappears at elevated temperatures. The density, self-diffusion coefficient, and shear viscosity for the Mg-La liquid alloy were predicted via DPMD simulation, the evolution patterns with Mg content and temperature were subsequently discussed, and a database was established accordingly. Finally, the mixing enthalpy and elemental activity of the Mg-La liquid alloy at 1200 K were reliably evaluated,which provides new guidance for related studies.

关键词： magnesium-lanthanum liquid alloys local structure macroscopic properties thermodynamic behavior deep potential mo-lecular dynamic simulation

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Toward understanding the role of genomic repeat elements in neurodegenerative diseases

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Neural Regeneration Research 2025年第3期20卷 646-659页

作者： Zhengyu An Aidi Jiang Jingqi Chen Institute of science and technology for Brain-Inspired Intelligence Fudan UniversityShanghaiChina MOE Frontiers center for Brain science Fudan UniversityShanghaiChina MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityShanghaiChina Zhangjiang Fudan international Innovation center ShanghaiChina

Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

关键词： Alzheimer's disease ataxia deep learning long-read sequencing neurodegeneration neurodegenerative diseases Parkinson's disease repeat element structural variant

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Constructing Pr-doped CoOOH catalytic sites for efficient electrooxidation of 5-hydroxymethylfurfural

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Journal of Energy Chemistry 2025年第1期100卷 234-244页

作者： Botao Fan Hao Zhang Bang Gu Feng Qiu Qiue Cao Wenhao Fang School of Chemical science and technology Yunnan University National center for international Research on Photoelectric and Energy Materials School of Materials and Energy Yunnan University

Electrocatalytic conversion of renewable biomass is emerging as a promising route for sustainable chemical production;hence it urgently calls for developing efficient electrocatalysts with low potentials and high current ***,a Pr-doped Co（OH）2hexagonal sheet （Pr/Co=1/9,in mole） is synthesized by electrodeposition as highly performant catalyst for 5-hydroxymethylfurfural （HMF） oxidation reaction（HMFOR） to produce 2,5-furandicarboxylic acid （FDCA）.This novel and low-cost catalyst possesses a rather low onset potential of 1.05 V （***） and requires only 1.10 V （***） to reach a current density of 10 mA cm-2for HMFOR,significantly outperforming Co（OH）2benchmark (i.e.,210 mV higher to reach10 m A cm-2).The origin of Pr promotion effect as well as the evolution of CoOOH catalytic sites and HMFOR process has been deeply elucidated by physical characterizations,kinetic experiments,in situ electrochemical techniques,and theoretical *** unique Pr-ameliorated CoOOH active centers enable 100%conversion of HMF,99.6%selectivity of FDCA,and 99.7%Faraday efficiency,with a superior cycling durability toward *** can be one of the most outstanding results for Co-based HMFOR catalysts to date in the *** this work can help open up new horizons for constructing novel and efficient Co-based electrocatalysts by the utilization of lanthanide elements.

关键词： Bioconversion

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Unraveling the significance of cobalt on transformation kinetics,crystallography and impact toughness in high-strength steels

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international Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 380-390页

作者： Yishuang Yu Jingxiao Zhao Xuelin Wang Hui Guo Zhenjia Xie Chengjia Shang Collaborative Innovation center of Steel technology University of Science and Technology BeijingBeijing 100083China School of Materials science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory for Advanced Metals and Materials University of Science and Technology BeijingBeijing 100083China

This work reveals the significant effects of cobalt(Co)on the microstructure and impact toughness of as-quenched highstrength steels by experimental characterizations and thermo-kinetic *** results show that the Co-bearing steel exhibits finer blocks and a lower ductile-brittle transition temperature than the steel without ***,the Co-bearing steel reveals higher transformation rates at the intermediate stage with bainite volume fraction ranging from around 0.1 to *** improved impact toughness of the Co-bearing steel results from the higher dense block boundaries dominated by the V1/V2 variant ***,the addition of Co induces a larger transformation driving force and a lower bainite start temperature(BS),thereby contributing to the refinement of blocks and the increase of the V1/V2 variant *** findings would be instructive for the composition,microstructure design,and property optimization of high-strength steels.

关键词： high-strength steel cobalt transformation kinetics crystallography impact toughness

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Research status and prospects of the fractal analysis of metal material surfaces and interfaces

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international Journal of Minerals,Metallurgy and Materials 2025年第1期32卷 20-38页

作者： Qinjin Dai Xuefeng Liu Xin Ma Shaojie Tian Qinghe Cui Beijing Advanced Innovation center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China Beijing Laboratory of Metallic Materials and Processing for Modern Transportation University of Science and Technology BeijingBeijing 100083China Laboratory for Advanced Materials Processing of Ministry of Education University of Science and Technology BeijingBeijing 100083China School of Mechanical and Electrical Engineering Xinjiang Institute of EngineeringUrumqi 830023China

As a mathematical analysis method,fractal analysis can be used to quantitatively describe irregular shapes with self-similar or self-affine *** analysis has been used to characterize the shapes of metal materials at various scales and *** methods make it difficult to quantitatively describe the relationship between the regular characteristics and properties of metal material surfaces and ***,fractal analysis can be used to quantitatively describe the shape characteristics of metal materials and to establish the quantitative relationships between the shape characteristics and various properties of metal *** the perspective of two-dimensional planes and three-dimensional curved surfaces,this paper reviews the current research status of the fractal analysis of metal precipitate interfaces,metal grain boundary interfaces,metal-deposited film surfaces,metal fracture surfaces,metal machined surfaces,and metal wear *** relationship between the fractal dimensions and properties of metal material surfaces and interfaces is *** from three perspectives of fractal analysis,namely,research scope,image acquisition methods,and calculation methods,this paper identifies the direction of research on fractal analysis of metal material surfaces and interfaces that need to be *** is believed that revealing the deep influence mechanism between the fractal dimensions and properties of metal material surfaces and interfaces will be the key research direction of the fractal analysis of metal materials in the future.

关键词： metal material surfaces and interfaces fractal analysis fractal dimension homogeneity

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Experimental Research on a Feasible Building Insulation Material Based Reed in china

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Journal of Thermal science 2025年第1期34卷 176-187页

作者： LIU Tiantian PENG Yizhe LIU Lifang CAI Chenghan WANG Shuang LI Hongqiang College of Civil Engineering Hunan University College of Civil Engineering Hunan University of Science and Technology School of Energy and Power Jiangsu University of Science and Technology National center for international Research Collaboration in Building Safety and Environment (NCIRCBSE) Hunan University Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education Hunan University

The seemingly useless reeds are prepared as thermal insulation materials,which not only meet the requirements of environmental sustainability but also enhance the added value of reeds,creating new economic *** hydrophobicity of raw biomass surfaces leads to problems such as weak bonding strength and non-dense structure in the formed materials,as well as issues related to the residual insect infestations on the *** this study,reed straw was used as the raw material,and foamed geopolymer was used as the binder to prepare building insulation materials based *** improve the interfacial adhesion performance between reed straw and foamed geopolymer,a thermochemical modification method-thermal carbonization,was *** this study,the mechanical properties and hydraulic properties of the studied materials with different degrees of surface thermal modification were tested,especially the fire resistance performance,and weathering resistance performance rarely found in published *** the surface thermal modification condition of reed straw was 250℃（30 min）,the comprehensive performance of reed-based building insulation materials was the best,when the studied material density was 321.3 kg/m3;the compressive strength was 0.59 MPa;the thermal conductivity was 0.101 W/（m·K）;the pH was 11.27;the moisture absorption rate was 25.1%,and the compressive strength loss rate in wet-dry cycles was 18.5%.In addition,it had excellent fire resistance performance and weathering resistance *** new material can be widely used to improve the thermal insulation of traditional buildings and as sandwich filler in prefabricated buildings,such as preparing insulating walls.

关键词： building insulation material based reed fire resistance performance interface adhesion properties pre-treatment of reeds weathering resistance performance

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Co/Co_(7)Fe_(3)heterostructures with controllable alloying degree on carbon spheres as bifunctional electrocatalyst forrechargeable zinc-air batteries

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international Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 476-487页

作者： Junkang Chen Yongyue Zhuang Yanxin Qiao Yu Zhang Aihua Yuan Hu Zhou School of Materials science and Engineering Jiangsu University of Science and TechnologyZhenjiang 212003China School of Environmental and Chemical Engineering Jiangsu University of Science and TechnologyZhenjiang 212003China

Exploring efficient and nonprecious metal electrocatalysts of oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)is crucial for developing rechargeable zinc-air batteries(ZABs).Herein,an alloying-degree control strategy was employed to fabricate nitrogen-doped carbon sphere(NCS)decorated with dual-phase Co/Co_(7)Fe_(3)heterojunctions(CoFe@NCS).The phase composition of materials has been adjusted by controlling the alloying *** optimal CoFe_(0.08)@NCS electrocatalyst displays a half-wave potential of 0.80 V for ORR and an overpotential of 283 mV at 10 mA·cm^(-2)for OER in an alkaline *** intriguing bifunctional electrocatalytic activity and durability is attributed to the hierarchically porous structure and interfacial electron coupling of highly-active Co_(7)Fe_(3)alloy and metallic Co *** the CoFe_(0.08)@NCS material is used as air-cathode catalyst of rechargeable liquid-state zinc-air battery(ZAB),the device shows a high peak power-density(157 mW·cm^(-2))and maintains a stable voltage gap over 150 h,outperforming those of the benchmark(Pt/C+RuO_(2))-based *** particular,the as-fabricated solid-state flexible ZAB delivers a reliable compatibility under different bending *** work provides a promising strategy to develop metal/alloy-based electrocatalysts for the application in renewable energy conversion technologies.

关键词： bifunctional electrocatalysts oxygen reduction reaction oxygen evolution reaction zinc-air battery metal/alloy carbon sphere

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A robust statistical prediction model for late-summer heavy precipitation days in North china

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science china Earth sciences 2025年第1期68卷 158-171页

作者： Shunli JIANG Tingting HAN Xin ZHOU Huijun WANG Zhicong YIN Xiaolei SONG Collaborative Innovation center on Forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education Nanjing University for Information Science and Technology Nansen-Zhu international Research Centre Institute of Atmospheric Physics Chinese Academy of Sciences Southern Marine science and Engineering Guangdong Laboratory (Zhuhai)

Recently, heavy precipitation(HP) events have occurred frequently in North china(NC), causing devastating economic losses and human fatalities. However, the short-term climate prediction of HP is quite limited. Combining year-to-year increment(DY) method and sliding correlations, we developed a robust seasonal prediction model for late-summer HP days(HPDs) in NC during 1982–2022, utilizing three independent predictors—February sea surface temperature(SST) in the Indian Ocean(SST＿IO), February snow depth over North Asia(SDE＿NA), and May melted snow depth in NC(MSDE＿NC). The SST＿IO anomalies affect NC's precipitation through the Pacific-Japan pattern. The SDE＿NA anomalies are associated with East Asian anomalous anticyclone by southeastern propagation of Rossby wave at Eurasia. The MSDE＿NC anomalies are followed by vertical motion and moisture anomalies in situ and thereby cause precipitation anomalies. This prediction model can well simulate the variations of the HPDs, with a correlation coefficient(CC) of 0.81(0.65) between the observed and predicted HPDs＿DY(HPDs＿anomaly). The percentage with the same sign for 15 extreme HPDs＿anomaly years(PSSE) is 100%.Moreover, in the cross-validation test during 1982–2022, the PSSE for HPDs＿anomaly is as high as 100%, along with a low rootmean-square error of 1.14. For independent hindcasts during 2013–2022, the CC between the observed and predicted HPDs＿DY(HPDs＿anomaly) is 0.93(0.83), together with high Nash-Sutcliffe efficiency(0.82) and agreement index(0.89). Specifically, the predictions are broadly consistent with the observations for 2015, 2016, 2017, 2021, and 2022, reflecting excellent performance of this prediction model of HPDs in NC.

关键词： Heavy precipitation at North china Year-to-year increment approach Robust seasonal prediction Sea surface temperature Snow depth

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Synergistically S/N self-doped biochar as a green bifunctional cathode catalyst in electrochemical degradation of organic pollutant

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Green Energy & Environment 2025年第1期10卷 214-230页

作者： Xuechun Wang Huizhong Wu Jiana Jing Ge Song Xuyang Zhang Minghua Zhou Raf Dewil Key Laboratory of Pollution Process and Environmental Criteria Ministry of EducationCollege of Environmental Science and EngineeringNankai UniversityTianjin300350China Tianjin Key Laboratory of Environmental technology for Complex Trans-Media Pollution Nankai UniversityTianjin300350China Tianjin Advanced Water Treatment technology international Joint Research center College of Environmental Science and EngineeringNankai UniversityTianjin300350China Department of Chemical Engineering KU LeuvenJ.De Nayerlaan 5Sint-Katelijne-Waver B2860Belgium

Biomass-derived heteroatom self-doped cathode catalysts has attracted considerable interest for electrochemical advanced oxidation processes(EAOPs)due to its high performance and sustainable ***,we illustrated the morphological fates of waste leaf-derived graphitic carbon(WLGC)produced from waste ginkgo leaves via pyrolysis temperature regulation and used as bifunctional cathode catalyst for simultaneous H_(2)O_(2) electrochemical generation and organic pollutant degradation,discovering S/N-self-doping shown to facilitate a synergistic effect on reactive oxygen species(ROS)*** the optimum temperature of 800℃,the WLGC exhibited a H_(2)O_(2) selectivity of 94.2%and tetracycline removal of 99.3%within 60 *** functional theory calculations and in-situ Fourier transformed infrared spectroscopy verified that graphitic N was the critical site for H_(2)O_(2) *** pyridinic N and thiophene S were the main active sites responsible for_OH generation,N vacancies were the active sites to produce ^(1)O_(2) from *** performance of the novel cathode for tetracycline degradation remains well under a wide pH range(3–11),maintaining excellent stability in 10 *** is also industrially applicable,achieving satisfactory performance treating in real water *** system facilitates both radical and non-radical degradation,offering valuable advances in the preparation of cost-effective and sustainable electrocatalysts and hold strong potentials in metal-free EAOPs for organic pollutant degradation.

关键词： Biochar S and N self-doping H_(2)O_(2)production In-situ FTIR Metal-free electrochemical advanced oxidation processes

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Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,china

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Atmospheric and Oceanic science Letters 2025年第1期18卷 18-23页

作者： Jiarui Cai Bo Sun Huijun Wang Yi Zheng Siyu Zhou Huixin Li Yanyan Huang Peishu Zong Collaborative Innovation center on forecast and Evaluation of Meteorological Disasters/Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment ChangeNanjing University of Information Science and TechnologyNanjingChina Southern Marine science and Engineering Guangdong Laboratory(Zhuhai) ZhuhaiChina china Meteorological Administration Key Laboratory of Transportation MeteorologyNanjingChina Jiangsu Meteorological Observatory NanjingChina

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical *** learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many *** this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,*** two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction *** addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

关键词： Groundwater depth Multi-head attention Improved dung beetle optimizer CNN-LSTM CNN-GRU Ningxia

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